Users reviewing online information (e.g., via a web page, a social media post, etc.) are often interested in translations of content items included in the online information. For example, users might be interested in translations of words, sentences, phrases, paragraphs, or even pages. To provide the translations to users on demand, as and when requested, it is desirable to provide computer-implemented machine translations of content items. Often, machine translations are generated using natural language processing (NLP) algorithms. NLP algorithms take a content item, including any item containing language including text, images, audio, video, or other multi-media, as input and generate a machine translation which is then presented to users. However, content items can be inaccurately translated due to, for example, variants of the same language (e.g., American English versus British English), different meanings of the same word, non-standard phrases (e.g., slang), etc. For example, the word “lift” can mean “move upward” among speakers of American English (as that word is commonly used in America), whereas it can mean “elevator” for British English speakers. A content item including the phrase, “press the button for the lift,” could be translated into either “press the button for the elevator” or “press the button to go up.” In addition, machine translations of a content item are often based on dictionary translations and do not consider context, which often makes a significant difference such as in slang or colloquial passages.